guidance on preparing a data management plan office of sponsored programs – boise state university

GUIDANCE ON PREPARING A DATA MANAGEMENT PLAN

Office of Sponsored Programs – Boise State University

Motivation for Data Sharing Plan Many federal funding agencies, including including

NIH and most recently NSFNIH and most recently NSF, are requiring that grant applications contain data management plans for projects involving data collection.

National Institutes of Health

The Final NIH Statement on Sharing Research Data was published in the NIH Guide on February 26, 2003. “Starting with the October 1, 2003 receipt date,

investigators submitting an NIH application seeking $500,000 or more in direct costs in any single year are expected to include a plan for data sharing or state why data sharing is not possible. “ Not part of peer review No later than the main findings from the final data set

are accepted for publication

National Science Foundation The National Science Foundation has

released a new requirement for proposal submissions regarding the management of data generated using NSF support. Starting in January, 2011, all proposals must include a data management plan (DMP). Up to 2 pages Plan will be reviewed What data are generated by your research? What is your plan for managing the data?

Elements of a Data Management PlanElement Description Recommended?

Data description

Provide a brief description of the information to be gathered; the nature, scope and scale of the data that will be generated or collected.

Highly recommended.

Generic Example 1:This project will produce public-use nationally representative survey data for the United States covering Americans' social backgrounds, enduring political predispositions, social and political values, perceptions and evaluations of groups and candidates, opinions on questions of public policy, and participation in political life.


Data description

Provide a brief description of the information to be gathered; the nature, scope and scale of the data that will be generated or collected.

Highly recommended.

Generic Example 2:This project will generate data designed to study the prevalence and correlates of DSM III-R psychiatric disorders and patterns and correlates of service utilization for these disorders in a nationally representative sample of over 8000 respondents. The sensitive nature of these data will require that the data be released through a restricted use contract.


Metadata A description of the metadata to be provided along with the generated data, and a discussion of the metadata standards used.

Highly recommended.

Generic Example 1:Metadata will be tagged in XML using the Data Documentation Initiative (DDI) format. The codebook will contain information on study design, sampling methodology, fieldwork, variable-level detail, and all information necessary for a secondary analyst to use the data accurately and effectively.


Existing data A survey of existing data relevant to the project and a discussion of whether and how these data will be integrated.

Generic Example 1:Few datasets exist that focus on this population in the United States and how their attitudes toward assimilation differ from those of others. The primary resource on this population, [give dataset title here], is inadequate because...


Existing data A survey of existing data relevant to the project and a discussion of whether and how these data will be integrated.

Generic Example 2:Data have been collected on this topic previously (for example: [add example(s)]). The data collected as part of this project reflect the current time period and historical context. It is possible that several of these datasets, including the data collected here, could be combined to better understand how social processes have unfolded over time.


Data organization

How the data will be managed during the project, with information about version control, naming conventions, etc. Generic Example 1:

Data will be stored in a CVS system and checked in and out for purposes of versioning. Variables will use a standardized naming convention consisting of a prefix, root, suffix system. Separate files will be managed for the two kinds of records produced: one file for respondents and another file for children with merging routines specified.


Quality Assurance

Procedures for ensuring data quality during the project.

Example 1:Quality assurance measures will comply with the standards, guidelines, and procedures established by the World Health Organization.


Quality Assurance

Procedures for ensuring data quality during the project.

Example 2:For quantitative data files, the [repository] ensures that missing data codes are defined, that actual data values fall within the range of expected values and that the data are free from wild codes. Processed data files are reviewed by a supervisory staff member before release.


Responsibility Names of the individuals responsible for data management in the research project.

Example 1:The project will assign a qualified data manager certified in disclosure risk management to act as steward for the data while they are being collected, processed, and analyzed.


Selection and retention periods

A description of how data will be selected for archiving, how long the data will be held, and plans for eventual transition or termination of the data collection in the future.

Example 1:Our project will generate a large volume of data, some of which may not be appropriate for sharing since it involves a small sample that is not representative. The investigators will work with staff of the [repository] to determine what to archive and how long the deposited data should be retained.


Audience Describe the audience of users for the data.

Example 1:The data to be produced will be of interest to demographers studying family formation practices in early adulthood across different racial and ethnic groups. In addition to the research community, we expect these data will be used by practioners and policymakers.


Access and sharing

Indicate how you intend to archive and share your data and why you have chosen that particular option.

Highly recommended.

This Element of the Data Management Plan will be covered by the Institutional template section


Intellectual property rights

Entities or persons who will hold the intellectual property rights to the data, and how IP will be protected if necessary. Any copyright constraints (e.g., copyrighted data collection instruments) should be noted.

Highly recommended.

This section of Data Management Plan with be covered by the Institutional template portion


Ethics and privacy

A discussion of how informed consent will be handled and how privacy will be protected, including any exceptional arrangements that might be needed to protect participant confidentiality, and other ethical issues that may arise.

Highly recommended.



Format Formats in which the data will be generated, maintained, and made available, including a justification for the procedural and archival appropriateness of those formats.

Highly recommended.

This Portion of the Data Management Plan will be covered by the institutional template portion of the document


Archiving and preservation

The procedures in place or envisioned for long-term archiving and preservation of the data, including succession plans for the data should the expected archiving entity go out of existence.

Highly recommended.



Storage and backup

Storage methods and backup procedures for the data, including the physical and cyber resources and facilities that will be used for the effective preservation and storage of the research data.

Highly recommended.



Security A description of technical and procedural protections for information, including confidential information, and how permissions, restrictions, and embargoes will be enforced.



Budget The costs of preparing data and documentation for archiving and how these costs will be paid. Requests for funding may be included.


guidance on preparing a data management plan office of sponsored programs – boise state university

Documents

management of data

generated data

data descriptionprovide

data collection

data sharing plan

data management plans

sharing research data

data management plan